scholarly journals Interpretation of non-linear empirical data-based process models using global sensitivity analysis

2011 ◽  
Vol 107 (1) ◽  
pp. 116-123 ◽  
Author(s):  
Tao Chen ◽  
Yanhui Yang
2005 ◽  
Vol 12 (3) ◽  
pp. 373-379 ◽  
Author(s):  
C. Tiede ◽  
K. Tiampo ◽  
J. Fernández ◽  
C. Gerstenecker

Abstract. A quantitative global sensitivity analysis (SA) is applied to the non-linear inversion of gravity changes and displacement data which measured in an active volcanic area. The common inversion of this data is based on the solution of the generalized Navier equations which couples both types of observation, gravity and displacement, in a homogeneous half space. The sensitivity analysis has been carried out using Sobol's variance-based approach which produces the total sensitivity indices (TSI), so that all interactions between the unknown input parameters are taken into account. Results of the SA show quite different sensitivities for the measured changes as they relate to the unknown parameters for the east, north and height component, as well as the pressure, radial and mass component of an elastic-gravitational source. The TSIs are implemented into the inversion in order to stabilize the computation of the unknown parameters, which showed wide dispersion ranges in earlier optimization approaches. Samples which were computed using a genetic algorithm (GA) optimization are compared to samples in which the results of the global sensitivity analysis are integrated by a reweighting of the cofactor matrix in the objective function. The comparison shows that the implementation of the TSI's can decrease the dispersion rate of unknown input parameters, producing a great improvement the reliable determination of the unknown parameters.


2014 ◽  
Vol 5 (2) ◽  
pp. 901-943 ◽  
Author(s):  
N. Bounceur ◽  
M. Crucifix ◽  
R. D. Wilkinson

Abstract. A global sensitivity analysis is used to describe the response of the Earth Climate Model of Intermediate Complexity LOVECLIM to components of the astronomical forcing (longitude of perihelion, obliquity, and eccentricity) assuming interglacial boundary conditions. Compared to previous studies, the sensitivity is global in the sense that it considers the full range of astronomical forcing that occurred during the Quaternary. We provide a geographical description of the variance due to the different components and their combinations and identify non-linear responses. The methodology relies on the estimation of sensitivity measures, which due to the computational cost of LOVECLIM cannot be obtained directly. Instead, we use a fast surrogate of the climate model, called an emulator, in place of the simulator. A space filling design (a maximin Latin hypercube constrained to span the range of astronomical forcings characterising the Pleistocene) is used to determine a set of experiments to run, which are then used to train a reduced-rank Gaussian process emulator. The simulator outputs considered are the principal modes of the annual mean temperature, precipitation, and the growing degree days, extracted using a principal component analysis. The experiments are run on two distinct land surface schemes to address the effect of vegetation response on climate. Sensitivity to initial conditions is also explicitly assessed. Precession and obliquity are found to contribute equally to growing degree days (GDD) in the Northern Hemisphere, and the effects of obliquity on the response of Southern Hemisphere temperature dominate precession effects. Further, compared to the original land-surface scheme with fixed vegetation, the LOVECLIM interactive vegetation induces non-linear responses in the Sahel-Sahara and Arctic sea-ice area. Finally, we find that there is no synergy between obliquity and precession.


Modelling ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 753-775
Author(s):  
John C. Chrispell ◽  
Eleanor W. Jenkins ◽  
Kathleen R. Kavanagh ◽  
Matthew D. Parno

Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.


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